code: def test_basics(self):
      self.run_layer_test(
          layers.RNN,
          init_kwargs={"cell": OneStateRNNCell(5, state_size=5)},
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 5),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={"cell": OneStateRNNCell(5, state_size=[5])},
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 5),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={"cell": OneStateRNNCell(5, state_size=(5,))},
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 5),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={
              "cell": OneStateRNNCell(5),
              "return_sequences": True,
          },
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 2, 5),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={
              "cell": OneStateRNNCell(5),
              "go_backwards": True,
              "unroll": True,
          },
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 5),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={"cell": TwoStatesRNNCell(5, state_size=[5, 5])},
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 5),
          expected_num_trainable_weights=3,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={"cell": TwoStatesRNNCell(5, state_size=(5, 5))},
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 5),
          expected_num_trainable_weights=3,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.RNN,
          init_kwargs={
              "cell": TwoStatesRNNCell(5),
              "return_sequences": True,
          },
          input_shape=(3, 2, 4),
          expected_output_shape=(3, 2, 5),
          expected_num_trainable_weights=3,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          supports_masking=True,
      )
  
imports: import numpy
  import pytest
  from keras import layers
  from keras import ops
  from keras import testing
label_desc: Use image node_id 1 for calling the RNNTest obj's underlying member method code with example usage: obj.test_basics() without return types